Two types of labs (tutorials) will be proposed for the course (alternating):
The solutions of the practical labs have to be submitted using the upload system
Week | Date | Topic | Lecturer | Materials | Deadline/Notes |
---|---|---|---|---|---|
1 | 23. 9. | — none — | |||
2 | 30. 9. | Seminar | BF | ||
3 | 7. 10. | Seminar: lecture 1,2 | VF | ||
4 | 14. 10. | Seminar: lecture 2,3 | VF | ||
5 | 21. 10. | Seminar: lecture 3,4 | VF/DB | ||
6 | 28. 10. | — National Holiday — | |||
7 | 4. 11. | Lab: SO Perceptron | VF/DB | ; DP tutorial | 2. 12. |
8 | 11. 11. | Seminar: Neural Networks | JD/DB | Matrix Differentiation | |
9 | 18. 11. | Lab: Neural Networks | JD | Code | 16. 12. |
10 | 25. 11. | Seminar: lecture 8 | BF/DB | ||
11 | 2. 12. | Lab: EM algorithm | BF/DB | task data | 07. 01. |
12 | 9. 12. | Seminar: lecture 9 | BF/DB | ||
13 | 16. 12. | Seminar: lecture 10 | BF/DB | Open learning room: Wed, 15.12., 17:00 KN:G-105 | |
14 | 6. 1. | Seminar: Ensembling | JD/DB |